Andrew Ng discusses the effectiveness of a multi-agent design pattern in AI, particularly for complex tasks like software development. This approach involves dividing a task into subtasks handled by different specialized agents (like software engineers, product managers, etc.).

Despite using the same large language model (LLM) to simulate different agents, this method has shown superior performance over single-agent approaches due to its ability to focus on specific subtasks. Ng highlights that this strategy mimics traditional management structures in companies, where complex projects are split and assigned to specialists. He suggests that this approach is useful in managing AI agents and mentions emerging frameworks and tools that support multi-agent systems, encouraging experimentation.

Professor Ng mentions frameworks and tools that support multi-agent systems, including AutoGen, Crew AI, LangGraph, and ChatDev.

Hereā€™s how I applied this pattern for learning Spanish words: I had a list of Spanish words in Anki. I took a screenshot and recognized them from it using Google Gemini app. After identifying the words, I asked it to create a story to aid in my learning. Additionally, I pasted the list into ChatGPT version 3.5, requesting it to craft another story. ChatGPTā€™s mobile app allows to ā€œRead aloudā€ the responses, aiding listening comprehension. This shows how different AI applications with different strengths can be used for learning foreign languages.

Another example: I used default Android voice recognition for the text above. Then, I submitted it to ChatGPT for error editing to highlight possible errors. After that, I copied it to Microsoft Copilot for further editing. The final version was edited in Lex editor.